Abstract
In the era of immunotherapy and personalized medicine, there is an urgent need for advancing the knowledge of immune evasion in different cancer types and identifying reliable biomarkers that guide both therapy selection and patient inclusion in clinical trials. Given the differential immune responses and evasion mechanisms in breast cancer, we expect to identify different breast cancer groups based on their expression of immune-related genes. For that, we used the sequential biclustering method on The Cancer Genome Atlas RNA-seq breast cancer data and identified 7 clusters. We found that 77.4% of the clustered tumor specimens evade through transforming growth factor-beta (TGF-β) immunosuppression, 57.7% through decoy receptor 3 (DcR3) counterattack, 48.0% through cytotoxic T-lymphocyte-associated protein 4 (CTLA4), and 34.3% through programmed cell death-1 (PD-1). TGF-β and DcR3 are potential novel drug targets for breast cancer immunotherapy. Targeting TGF-β and DcR3 may provide a powerful approach for treating breast cancer because 57.7% of patients overexpressed these two molecules. Furthermore, triple-negative breast cancer (TNBC) patients clustered equally into two subgroups: one with impaired antigen presentation and another with high leukocyte recruitment but four different evasion mechanisms. Thus, different TNBC patients may be treated with different immunotherapy approaches. We identified biomarkers to cluster patients into subgroups based on immune evasion mechanisms and guide the choice of immunotherapy. These findings provide a better understanding of patients' response to immunotherapies and shed light on the rational design of novel combination therapies.